International Series in Operations Research & Management Science
              Volume              1052007
Process OptimizationA Statistical Approach Authors:

• Professor Enrique Del Castillo
• …show all 1hide
  

ISBN: 978-0-387-71434-9 (Print) 978-0-387-71435-6  (Online)

About this textbook

• A much stronger treatment of the topic than the Wiley books published in this area, making this text a must-have for students and lecturers alike
•                                         Provides a complete exposition of mainstream experimental design techniques and response surface methods
•                                         Contains a mix of technical and practical sections, appropriate for a first year graduate text in the subject or useful for self-study or reference
•                                         Presents a detailed treatment of Bayesian Optimization approaches based on experimental data and includes an introduction to Bayesian inference
  

PROCESS OPTIMIZATION: A Statistical Approach
is a textbook for a course in experimental optimization techniques for industrial production processes and other "noisy" systems where the main emphasis is process optimization. The book can also be used as a reference text by Industrial, Quality and Process Engineers and Applied Statisticians working in industry, in particular, in semiconductor/electronics manufacturing and in biotech manufacturing industries.
The major features of PROCESS OPTIMIZATION: A Statistical Approach are:

• It provides a complete exposition of mainstream experimental design techniques, including designs for first and second order models, response surface and optimal designs;
• Discusses mainstream response surface method in detail, including unconstrained and constrained (i.e., ridge analysis and dual and multiple response) approaches;
• Includes an extensive discussion of Robust Parameter Design (RPD) problems, including experimental design issues such as Split Plot designs and recent optimization approaches used for RPD;
• Presents a detailed treatment of Bayesian Optimization approaches based on experimental data (including an introduction to Bayesian inference), including single and multiple response optimization and model robust optimization;
• Provides an in-depth presentation of the statistical issues that arise in optimization problems, including confidence regions on the optimal settings of a process, stopping rules in experimental optimization and more;
• Contains a discussion on robust optimization methods as used in mathematical programming and their application in response surface optimization;
• Offers software programs written in MATLAB and MAPLE to implement Bayesian and frequentist process optimization methods;
• Provides an introduction to the optimization of computer and simulation experiments including and introduction to stochastic approximation and stochastic perturbation stochastic approximation (SPSA) methods;
• Includes an introduction to Kriging methods and experimental design for computer experiments;
  

Provides extensive appendices on Linear Regression, ANOVA, and Optimization Results.